Ribosome hibernation preserves translation machinery during stress, yet its mechanisms in Archaea remain poorly defined. Using cryo-EM analysis, we studied hibernation pathways in Pyrococcus abyssi stressed cells. We identified HibA, a previously unrecognized family of hibernation factors widespread in Archaea. HibA consists of a bacterial-like HPF/RaiA domain fused to a Cystathionine Beta Synthase module. Unexpectedly, HibA binds to the ribosome in three different conformations, occupying the A, P and E sites of tRNAs, as well as that of mRNA, enhancing its ability to protect the ribosome from degradation. Idle ribosomes also frequently accumulate the archaeal homolog of eukaryotic ribosome maturation protein SBDS (aSBDS), suggesting that stressed archaeal cells may engage parallel hibernation routes in which aSBDS can complement HibA. Deletion of hibA in Thermococcus barophilus delays recovery from stationary phase and reduces 70S ribosome pools, establishing its role in ribosome preservation. Taxonomic profiling shows that many archaeal lineages encode distinct repertoires of ribosome-associated protection factors, underscoring the modular and multi-layered nature of archaeal hibernation systems. In addition, a comprehensive phylogenetic analysis highlights the evolutionary relationships between prevalent ribosome hibernation factors across Bacteria and Archaea. Ribosome hibernation preserves translation machinery during stress. Here, the authors identify a ribosome hibernation factor that is widespread in archaea and combines bacterial HPF/RaiA-like and cystathionine beta-synthase domains.

Freshwater lakes are globally significant sources of potent greenhouse gases (GHGs), but how their GHGs emissions respond to changing nutrient levels remains unclear. Here, we demonstrated that nitrous oxide (N2O) production pathways in lake sediments are tightly linked to trophic state, whereas methane (CH4) production appears to be multifactorial Through global metagenomics and controlled batch experiments. In eutrophic sediments, N2O is efficiently removed through complete denitrification, with nitrification serving as the main production pathway, whereas oligotrophic sediments produce N2O primarily via incomplete denitrification. By simulating nutrient transitions using a cross-inoculation experiment, we further revealed that lake sediments systematically shift between these N2O production pathways as their trophic state changes, from denitrification-driven to nitrification-dominated during eutrophication, with the inverse pattern during oligotrophication. Consequently, N2O emissions can be effectively mitigated by inhibiting nitrification in eutrophic lakes and restricting incomplete denitrification in oligotrophic ones. Our findings establish trophic status as a key driver of N2O production sources in lake sediments. Trophic status is a critical regulator of N2O dynamics in freshwater lake sediments, with implications for predicting GHG fluxes under global change scenarios.

The Gut microbiome-virome dynamics and interactions Collection highlights gut viruses, mainly bacteriophages, as determinants of microbial community structure and host-relevant functions across human, animal, and environmental systems. The Collection welcomes studies that quantify virus-microbe interactions, evidence-based linking viruses to microbial hosts, characterise infection dynamics, and connect them to ecological or clinical outcomes. It prioritises methodological rigour and translational relevance in diagnostics, surveillance, and phage-based interventions.

Gene expression analysis has evolved substantially over the past 25 years, from early transcript surveys using expressed sequence tags and microarrays to RNA sequencing, and more recently to single-cell and spatial transcriptomics. These successive waves have expanded measurement scale and resolution, enabling systematic discovery of transcriptional programmes, inference of gene regulatory networks, and increasingly direct links between transcriptomic insight and therapeutic strategies that modulate gene expression. In this Perspective, we synthesize major methodological milestones with bibliometric trends in leading bioinformatics journals to describe four revolutions that redefined gene expression analysis. We also map widely used computational tools onto a common timeline by analysing 70 78 831 open-access full-text articles, illustrating how enduring statistical frameworks coexist with rapidly growing end-to-end analysis ecosystems. We highlight current challenges and emerging directions in core bioinformatics approaches for gene expression analysis. Looking ahead, we argue that the next era will be defined less by generating new datasets and more by organizing, searching, and reusing transcriptomic and multimodal information at scale. We propose three future directions: consortium-scale searchable transcriptomic knowledgebases, foundation models for gene expression analysis, and programmable regulatory design for engineered control of gene expression. The landscape of gene expression analysis is shifting from descriptive measurement towards queryable, predictive, and programmable gene expression biology.

Recent advances in Machine Learning have transformed antibody development through in silico models, accelerating therapeutic candidate identification. However, challenges persist: rapid adaptation of property predictors to laboratory-specific assays with incomplete datasets; batch effects introducing systematic bias; assay costs necessitating efficient unseen property prediction. We introduce a novel multimodal architecture featuring specialized tokenization and embedding projection that integrates text and protein language models (pLM) and a learning strategy to enable context-conditioned multi-property prediction without learning shortcuts. Our framework enables prompting without dictionary merging across modalities, creating a compact model capable of context-conditioned learning for multi-property prediction. The orchestrating model avoids pLM-to-text projection while enabling inference-time adaptation without retraining. Using 876,898 antibody heavy chain sequences with batch effect simulation, our architecture achieved Spearman’s ρ > 0.8 across multiple developability properties, significantly outperforming fine-tuned multimodal LLMs and showed the ability to leverage correlation between properties for prediction. This approach has the potential to address critical antibody development challenges.

Chemotaxis is the directional motion of objects in response to chemical gradients, a process that drives navigation in micro/nanomotors, enabling a bio-inspired transition toward autonomous direction control. However, current studies lack consistent mechanistic justification, definition, and standardized experimental validation. This review summarizes key physical principles governing active chemotaxis, surveys experimental strategies for generating chemical gradients and quantifying responses, and examines how individual chemotactic mechanisms and long-range, anisotropic chemical interactions give rise to emergent collective behaviors, while outlining prospective applications. Together, these efforts establish a principled engineering framework for advancing chemotaxis as a robust functional navigation modality in synthetic micro/nanomotors. This Review Article examines artificial chemotaxis in micro/nanomotors, detailing mechanisms, experimental strategies, and collective behaviors. It highlights the need for standardized validation and explores applications in biomedical and industrial fields, emphasizing the challenges and potential of synthetic chemotaxis.

Gram staining has guided microbiology for over a century by coloring cells purple or pink, a read-out thought to distinguish monoderms (single membrane, thick peptidoglycan) from diderms (inner and outer membranes with thin wall). Here we show this rule fails repeatedly across Bacillaceae lineages historically deemed “Gram-positive”. By combining light and transmission-electron microscopy, antibiotic-sensitivity assays and comparative genomics across 57 strains, we identify “Gram-negative-staining monoderms” lacking outer membrane yet retaining thick peptidoglycan walls. These bacteria lack lipopolysaccharide- and β-barrel assembly-genes and remain highly susceptible to vancomycin and lysozyme (agents normally excluded by diderm envelopes), demonstrating functional monoderm status. Surprisingly, teichoic-acid biosynthetic pathways are patchily distributed and do not predict staining behavior. This discovery calls into question the textbook purple-or-pink dichotomy, decoupling stain color from membrane architecture. Clinically, misidentifying pink-staining Bacillaceae (including emerging pathogens such as Bacillus infantis) risks inappropriate therapy, whereas genome-guided diagnostics enable precise antibiotic stewardship. Genomic and microscopy evidence shows that the Bacillaceae, assumed to only stain Gram-positive, actually have some lineages staining Gram-negative, yet unexpectedly maintaining a thick wall and no outer membrane, challenging Gram staining assumptions.

The efficient extraction of electrons from photosynthetic microorganisms remains a critical challenge in living biophotovoltaics (BPV). While nanomaterials can facilitate electron transport, their stochastic adsorption leads to inefficient material-wasteful interfaces. Here, we demonstrate a controllable approach to direct the targeted assembly of gold nanoparticles (AuNPs) onto the type IV pili of Synechocystis sp. PCC 6803 by using a genetically encoded gold-binding peptide. This approach creates a spatially precise conductive nano-bio interface on the cell envelope that serves as a dedicated electron conduit between photosynthetic electron transport chains (PETCs) and electrodes. This nano-bio interface enhances electron transfer through synergistic improvements in interfacial charge transfer and biofilm density, ultimately yielding a four-fold increase in photocurrent density, while using two orders of magnitude less gold than non-targeted strategies. Moreover, the AuNPs can be transferred from inactivated to fresh cells, indicating a potential pathway for long-term stability. This work establishes a generalizable strategy for the rational design of conductive interfaces on living cells, with implications for biophotovoltaics, microbial electrosynthesis, and next-generation biohybrid devices. Inefficient extracellular electron transfer of cyanobacteria impedes power output in biophotovoltaics. Here, the authors assemble a gold conductive layer on the cell surface by attaching gold-binding peptide to the pili structure, achieving a four-fold increase in photocurrent generation.